

from __future__ import annotations

from cmath import sqrt

def 			lowerCamelCase__			(       _a				,      _a				,      _a):

	if a == 0:
		raise ValueError("Coefficient 'a' must not be zero.")
	SCREAMING_SNAKE_CASE :   Tuple					  =							b * b - 4 * a * c

	SCREAMING_SNAKE_CASE :   Dict					  =							(-b + sqrt(_a)) / (2 * a)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							(-b - sqrt(_a)) / (2 * a)

	return (
	    root_a.real if not root_a.imag else root_a,
	    root_a.real if not root_a.imag else root_a,
	)

def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							quadratic_roots(a=5				,      b=6				,      c=1)
	print(f"The solutions are: {solutiona} and {solutiona}")


if __name__ == "__main__":
			main()







"""simple docstring"""


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			MobileBertConfig.from_json_file(a__ )
						print(F"""Building PyTorch model from configuration: {config}""" )
						lowerCAmelCase   :Tuple						=			MobileBertForPreTraining(a__ )
						# Load weights from tf checkpoint
						lowerCAmelCase   :Any						=			load_tf_weights_in_mobilebert(a__     ,      a__     ,      a__ )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}""" )
						torch.save(model.state_dict()     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--mobilebert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained MobileBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)